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Background: Atrial fibrillation (AF) is associated with a five-fold increased risk of ischemic stroke. A portion of this risk is heritable; however, current risk stratification tools (CHA 2 DS 2 -VASc) do not include family history or genetic risk. We hypothesized that we could improve ischemic stroke prediction in patients with AF by incorporating polygenic risk scores (PRS). Methods: Using data from the largest available genome-wide association study in Europeans, we combined over half a million genetic variants to construct a PRS to predict ischemic stroke in patients with AF. We externally validated this PRS in independent data from the UK Biobank, both independently and integrated with clinical risk factors. The integrated PRS and clinical risk factors risk tool had the greatest predictive ability. Results: Compared with the currently recommended risk tool (CHA 2 DS 2 -VASc), the integrated tool significantly improved Net Reclassification Index (2.3% [95% CI, 1.3%–3.0%]) and fit (χ 2 P =0.002). Using this improved tool, >115 000 people with AF would have improved risk classification in the United States. Independently, PRS was a significant predictor of ischemic stroke in patients with AF prospectively (hazard ratio, 1.13 per 1 SD [95% CI, 1.06–1.23]). Lastly, polygenic risk scores were uncorrelated with clinical risk factors (Pearson correlation coefficient, −0.018). Conclusions: In patients with AF, there appears to be a significant association between PRS and risk of ischemic stroke. The greatest predictive ability was found with the integration of PRS and clinical risk factors; however, the prediction of stroke remains challenging.

Coronary artery disease (CAD) is a leading cause of death, yet its genetic determinants are not fully elucidated. We report a multi-ethnic genome-wide association study of CAD involving nearly a quarter of a million cases, incorporating the largest cohorts to date of Whites, Blacks, and Hispanics from the Million Veteran Program with existing studies including CARDIoGRAMplusC4D, UK Biobank, and Biobank Japan. We verify substantial and nearly equivalent heritability of CAD across multiple ancestral groups, discover 107 novel loci including the first nine on the X-chromosome, identify the first eight genome-wide significant loci among Blacks and Hispanics, and demonstrate that two common haplotypes are largely responsible for the risk stratification at the well-known 9p21 locus in most populations except those of African origin where both haplotypes are virtually absent. We identify 15 loci for angiographically derived burden of coronary atherosclerosis, which robustly overlap with the strongest and earliest loci reported to date for clinical CAD. Phenome-wide association analyses of novel loci and externally validated polygenic risk scores (PRS) augment signals from the insulin resistance cluster of risk factors and consequences, extend previously established pleiotropic associations of loci with traditional risk factors to include smoking and family history, and confirm a substantially reduced transferability of existing PRS to Blacks. Downstream integrative genomic analyses reinforce the critical role of endothelial, fibroblast, and smooth muscle cells within the coronary vessel wall in CAD susceptibility. Our study highlights the value of a multi-ethnic design in efficiently characterizing the genetic architecture of CAD across all human populations.

Background: The pathophysiology of lipoprotein (a) [Lp(a)] remains obscure. We conducted a phenome wide analysis study (PheWAS) to identify pleiotropic effects of LPA variants. Methods: Among 51,843 European-ancestry participants (age 58±16 years, 54% female) of the electronic MEdical Records and Genomics (eMERGE) network we assessed whether LPA variants exhibited pleiotropic affects through an electronic health record-based PheWAS. We adjusted significant associations by presence of atherosclerotic cardiovascular disease (ASCVD; defined as a composite of coronary heart disease, cerebrovascular disease, and peripheral artery disease) to determine whether those associations were independent of ASCVD. Results: In PheWAS analysis, we tested 864 phecodes with 36 independent LPA variants. We identified 21 significant associations with non-ASCVD phenotypes including heart failure, aortic valve stenosis, and chronic kidney disease of which 14 were replicated across independent cohorts. The strength of associations between the LPA variant rs10455872 and both heart failure and aortic valve stenosis related phecodes were significantly attenuated after adjustment for ASCVD. However, the association with chronic kidney disease phecode remained independent following adjustment for ASCVD without any attenuation in the estimated odds ratio (Table). Conclusions: LPA genetic variants were associated with aortic valve stenosis, heart failure and chronic kidney disease. The observed association of LPA variant rs10455872 with aortic stenosis and heart failure appear to be partially mediated by ASCVD, while the association with chronic kidney disease appears to be independent of ASCVD. Additional studies are needed to elucidate potential mechanisms by which Lp(a) may contribute to the pathogenesis of non-ASCVD disease phenotypes.

Introduction: Atrial fibrillation (AF) is associated with a five-fold increased risk of ischemic stroke. A portion of this risk is heritable, however current risk stratification tools (CHA 2 DS 2 -VASc) don’t include family history or genetic risk. Hypothesis: A polygenic risk scores (PRS) is both independently, and in integrated with clinical risk factors, predictive of ischemic stroke in patients with Atrial Fibrillation. Methods: Using data from the largest available GWAS in Europeans, we combined over half a million genetic variants to construct a PRS to predict ischemic stroke in patients with AF. We externally validated this PRS in independent data from the UK Biobank (UK Biobank), both independently and integrated with clinical risk factors. Results: The integrated PRS and clinical risk factors risk tool had the greatest predictive ability. Compared with the currently recommended risk tool (CHA 2 DS 2 -VASc), the integrated tool significantly improved net reclassification (NRI: 2.3% (95%CI: 1.3% to 3.0%)), and fit (χ2 P =0.002). Independently, PRS was a significant predictor of ischemic stroke in patients with AF prospectively (Hazard Ratio: 1.13 per 1 SD (95%CI: 1.04 to 1.21)). Lastly, polygenic risk scores were uncorrelated with clinical risk factors (Pearson’s correlation coefficient: -0.018). Conclusions: In patients with AF, there appears to be a significant association between PRS and risk of ischemic stroke. The greatest predictive ability was found with the integration of PRS and clinical risk factors, however the prediction of stroke remains challenging.

Introduction: Familial Hypercholesterolemia (FH) variants confer risk for coronary artery disease (CAD) even after adjusting for a single baseline LDL measure. Hypothesis: We hypothesized that a model incorporating serial LDL measures over many years may account for the risk associated with FH variants. Methods: We analyzed 418,790 participants in the Million Veteran Program with electronic health records (EHR) spanning up to 15 years prior to and 7 years after enrollment. FH variants were defined using ClinVar and standard bioinformatic approaches. Carriers were identified by custom genotype array. CAD cases were identified by chart codes for acute MI and coronary revascularization. We used a nested case-control design conditional on survival to enrollment. Controls were matched on year of first LDL, time between first LDL and the case index date, age, sex, and ancestry. Logistic regression was used to estimate CAD risk among FH carriers while adjusting for CAD risk factors and the first, maximum (max), or mean LDL prior to the index date. Results: We found 53 LDLR , 2 APOB , and 2 PCSK9 variants, and FH prevalence was 1:303. We identified 23,091 cases with ≥1 LDL measure prior to the diagnosis of CAD and matched 10 controls per case. Cases had a median of 6 LDL measures over a median of 49 months prior to the index date. Carriers had an increased risk for CAD compared to non-carriers. Adjusting for the first, max, and mean LDL progressively attenuated risk of CAD associated with FH, but residual FH risk remained substantial ( Figure ). This pattern did not change in the subset of subjects with the most extensive LDL data nor in the subset of incident cases. We additionally found evidence of modifying effects of sex and ancestry, with higher within-group risk for females and subjects of African ancestry ( Figure ). Conclusion: The risk associated with FH variants cannot be fully captured by the LDL data available in the EHR, even when considering multiple LDL measurements spanning several years.

AimsFamilial hypercholesterolemia (FH) genetic variants confer risk for coronary artery disease (CAD) even after adjusting for low-density lipoprotein cholesterol (LDL-C) levels, using a single measurement. This study evaluated whether multiple historical measures of LDL-C observed in the electronic health record (EHR) can account for the risk associated with FH variants.Methods and ResultsWe analyzed 418,790 participants in the Million Veteran Program with EHR data spanning up to 15 years prior to and 7 years after enrollment, including ∼6.3 million LDL-C measurements. FH variants in LDLR, APOB, and PCSK9 were identified using a custom genotype array. We implemented a nested case-control design, using incidence density sampling to match etiologic exposure windows and measure CAD risk while adjusting for LDL-C exposure. In a cohort of 23,091 primarily prevalent cases and 230,910 matched controls, FH variants conferred increased risk for CAD (odds ratio: 1.53; 95% confidence interval: 1.24 to 1.89; p: 7.8×10−5). Adjusting for mean LDL-C exposure prior to the index date attenuated this risk more than adjusting for a single measurement, but significant risk remained (odds ratio: 1.33; 95% confidence interval: 1.08 to 1.64; p = 8.4×10−3). The pattern was also apparent in stratified analyses by sex and ancestry, and we found evidence of an interaction between sex and FH carrier status.ConclusionThe risk associated with FH variants cannot be fully captured by the LDL-C data available in the EHR, even when considering multiple LDL-C measurements spanning more than a decade.

BackgroundAtrial fibrillation (AF) is associated with a five-fold increased risk of ischemic stroke. A portion of this risk is heritable, however current risk stratification tools (CHA2DS2-VASc) don’t include family history or genetic risk. We hypothesized that we could improve ischemic stroke prediction in patients with AF by incorporating polygenic risk scores (PRS).ObjectivesTo construct and test a PRS to predict ischemic stroke in patients with AF, both independently and integrated with clinical risk factors.MethodsUsing data from the largest available GWAS in Europeans, we combined over half a million genetic variants to construct a PRS to predict ischemic stroke in patients with AF. We externally validated this PRS in independent data from the UK Biobank (UK Biobank), both independently and integrated with clinical risk factors.ResultsThe integrated PRS and clinical risk factors risk tool had the greatest predictive ability. Compared with the currently recommended risk tool (CHA2DS2-VASc), the integrated tool significantly improved net reclassification (NRI: 2.3% (95%CI: 1.3% to 3.0%)), and fit (χ2 P =0.002). Using this improved tool, >115,000 people with AF would have improved risk classification in the US. Independently, PRS was a significant predictor of ischemic stroke in patients with AF prospectively (Hazard Ratio: 1.13 per 1 SD (95%CI: 1.06 to 1.23))). Lastly, polygenic risk scores were uncorrelated with clinical risk factors (Pearson’s correlation coefficient: −0.018).ConclusionsIn patients with AF, there appears to be a significant association between PRS and risk of ischemic stroke. The greatest predictive ability was found with the integration of PRS and clinical risk factors, however the prediction of stroke remains challenging.

Introduction: Recent studies suggest a substantial improvement in risk prediction of coronary artery disease (CAD) with polygenic risk scores (PRS). The degree to which this improvement is generalizable to other European (EUR) and non-EUR populations remains unclear. Hypothesis: We hypothesized that genome-wide PRSs trained and validated in the UK Biobank (UKBB) would perform best in external EUR populations and reduce the performance gap in prediction between EURs and African Americans (AA). Methods: We tested our hypothesis in ~91 000 EUR, AA, and Hispanic (HISP) Million Veteran Program (MVP) participants with CAD and ~225 000 non-cases as of July 2017. We created 5 separate weighted PRSs in all participants using: 1) 164 genome-wide significant SNPs for CAD (164SNPs), 2) 46 000 Metabochip SNPs (GRS46K), 3) 1.5 million (M) SNPs from standard pruning & thresholding of CARDIOGRAM+C4D (Khera_P&T), 4) 6.6M SNPs PRS created with LDpred (LDpred_Khera), and 5) 1.7M SNP PRS combining weights of 3 PRSs through meta-analysis (meta-GRS). We estimated age & sex adjusted Odds Ratios (OR) of CAD per standard deviation increase in each PRS using logistic regression and performed sensitivity analyses in subgroups of cases and non-cases. Results: LDpred and meta-GRS performed best among EURs (figure). However, the point estimate of the OR for these PRSs in MVP was notably lower than that observed in the UKBB (1.36 vs. ~1.7). ORs in MVP were higher among participants with early-onset disease (1.39), myocardial infarction (1.52), and revascularization (1.56) but lower for incident events after enrollment (1.27). Performance in HISP was mildly diluted but all PRS performed poorly among AA (figure). Conclusion: Genome-wide PRSs developed in the UKBB transfer best to external EUR populations but may over-estimate risk and do not close the performance gap in prediction between EURs and AAs. Our findings highlight a need for better calibrated PRSs specific to the cohort and race/ethnic group being tested prior to implementation in clinical practice.

BACKGROUND: Insulin resistance may be present in healthy adults and is associated with poor health outcomes. Obesity is a risk factor for insulin resistance, but most obese adults do not have insulin resistance. Fitness may be protective, but the association between fitness, weight, and insulin resistance has not been studied in a large population of healthy adults. METHODS: A cross-sectional analysis of cardiorespiratory fitness, body mass index, and markers of insulin resistance was performed. Study participants were enrolled at the Cooper Clinic in Dallas, Texas. The analysis included 19,263 women and 48,433 men with no history of diabetes or cardiovascular disease. Cardiorespiratory fitness was measured using exercise treadmill testing. Impaired fasting glucose (100–125 mg/dL) and elevated fasting triglycerides (≥150 mg/dL) were used as a markers of insulin resistance. RESULTS: Among individuals with normal weight, poor fitness was associated with 2.2-fold higher odds of insulin resistance in women (1.4–3.6; P = .001) and 2.8-fold higher odds in men (2.1–3.6; P

Genome-wide association studies (GWAS) have been the primary tool for unbiased assessment of the genetic basis of coronary artery disease (CAD) for more than a decade. We summarize successes as well as shortcomings of recent studies in this context. The number of CAD-associated loci has more than doubled in the past year to 161. This rapid progress has been in large part due to the release of genome-wide genotyping data for the largely European participants of the UK Biobank study which has been combined with existing GWAS from the CARDIoGRAMplusC4D consortium. Additional discoveries have been achieved through large-scale genotyping of participants using custom high-yield genotyping arrays including the Metabochip and the Exome chip. As a consequence, the ability of genetic risk scores in predicting incident CAD events has improved but that improvement has only been shown in European populations. GWAS have proven to be a fruitful approach for uncovering the genetic drivers of CAD. However, determining the mechanisms of association of GWAS findings remains a challenging endeavor requiring long-term investment. Genetic risk scores offer an opportunity for recent findings to have an immediate clinical impact. Going forward, CAD genetics will benefit greatly from the release of more genetic data produced by mega-biobanks. These new data will allow for the more comprehensive examination of underrepresented populations.